title: Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis.
abstract: Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.
authors.affiliation: Methodology of Educational Sciences Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Andreas Vesaliusstraat 2, 3000, Leuven, Belgium, Kim.DeRoover@ppw.kuleuven.be.
authors.forename: Kim
authors.initials: K
authors.lastname: De Roover
authors.forename: Eva
authors.initials: E
authors.lastname: Ceulemans
authors.forename: Marieke E
authors.initials: ME
authors.lastname: Timmerman
authors.forename: John B
authors.initials: JB
authors.lastname: Nezlek
authors.forename: Patrick
authors.initials: P
authors.lastname: Onghena
mesh_terms: D006801:Humans; D015233:Models, Statistical; D011594:Psychometrics / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*